--- title: "Jingwen Pu — author profile and papers — Elamur Research" description: "Jingwen Pu, University of Electronic Science and Technology of China: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/jingwen-pu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jingwen-pu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jingwen Pu # Jingwen Pu Jingwen Pu has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Zhaokun Wang](https://md.research.elamur.ai/free/en/authors/zhaokun-wang/), [Jie Ou](https://md.research.elamur.ai/free/en/authors/jie-ou/), [Jinyu Guo](https://md.research.elamur.ai/free/en/authors/jinyu-guo/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5005917928) · [ORCID](https://orcid.org/0009-0003-3506-1516) · [**h-index 1**](https://openalex.org/authors/A5005917928) · [13 works](https://openalex.org/works?filter=author.id:A5005917928) · [4 citations](https://openalex.org/works?filter=author.id:A5005917928&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/jingwen-pu/#papers) · [⭐ Paper Stars ranking: #12057](https://md.research.elamur.ai/free/en/paper-stars/page/121/#star-12057) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Jingwen+Pu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jingwen+Pu) · [dblp](https://dblp.org/search?q=Jingwen+Pu) · [ACL Anthology](https://aclanthology.org/search/?q=Jingwen+Pu) ## Affiliations [🎓**University of Electronic Science and Technology of China** Chengdu · China 2025–2026 · 10 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) · [🎓**Zhejiang University** Hangzhou · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/jingwen-pu/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/)** *(2026)* LLMs trained on uncurated data can memorize sensitive or hazardous facts. Most unlearning techniques remove such knowledge by editing weights, which is costly, can damage general ability, and does not work for closed-source models. CAP takes a different route: the target LLM stays frozen, and a small language model… 2. **[Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference](https://md.research.elamur.ai/free/en/papers/pu-2026-decoding-unlearning-fact-forgetting-entropy/)** *(2026)* Large language models memorize copyrighted, private, or hazardous facts. Most unlearning methods remove them by fine-tuning, which is expensive and irreversible and requires forget and retain datasets. Pu et al. propose SEGUE, which forgets at inference time with no training and no change to weights. It has two stages: ## Co-authors here 6 co-authors with a page, the most papers together first. - [Zhaokun Wang](https://md.research.elamur.ai/free/en/authors/zhaokun-wang/) ×2 - [Jie Ou](https://md.research.elamur.ai/free/en/authors/jie-ou/) - [Jinyu Guo](https://md.research.elamur.ai/free/en/authors/jinyu-guo/) - [Wenhong Tian](https://md.research.elamur.ai/free/en/authors/wenhong-tian/) - [Wenyi Li](https://md.research.elamur.ai/free/en/authors/wenyi-li/) - [Xinyu Zhang](https://md.research.elamur.ai/free/en/authors/xinyu-zhang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 Updated 1 November 2026